class_docstr: A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
  have unlimited prompt length and assign weights to tokens in prompt.
docstr: Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
  length, in tokens, of all texts.
docstr_tok: ["Accepts","a","list","of","texts","and","calls","tokenize_line","(",")","on","each",",","with","cache",".","Returns","the","list","of","results","and","maximum","length",",","in","tokens",",","of","all","texts","."]
code: def process_texts(self, texts):
      """
      Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
      length, in tokens, of all texts.
      """
  
      token_count = 0
  
      cache = {}
      batch_chunks = []
      for line in texts:
          if line in cache:
              chunks = cache[line]
          else:
              chunks, current_token_count = self.tokenize_line(line)
              token_count = max(current_token_count, token_count)
  
              cache[line] = chunks
  
          batch_chunks.append(chunks)
  
      return batch_chunks, token_count
  
imports: import math
  from collections import namedtuple
  import torch
  from modules import prompt_parser, devices, sd_hijack
  from modules.shared import opts
label_desc: Use image node_id 8 for calling the FrozenCLIPEmbedderWithCustomWordsBase obj's underlying member method code with example usage: obj.process_texts(texts) and returns: batch_chunks,  token_count